A novel hybrid model based on neural network and multi-objective optimization for effective load forecast

Priyanka Singh and Pragya Dwivedi

Energy, 2019, vol. 182, issue C, 606-622

Abstract: In recent years, increased attention has been paid by the researchers to predict accurate and stable load due to its effect on the economy and need for proper management of power systems. However, most of the previous research focused only on either reducing load forecast error or enhancing the stability, very few studies focused on these two issues simultaneously. Introducing a forecasting model to solve both independent objectives at the same time is a challenging task due to the complex behavior of the load pattern.

Keywords: Load forecasting; Artificial neural network; Multi-objective optimization; Hybrid forecasting model; Multi-objective follow the leader (MOFTL) (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421931206X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:182:y:2019:i:c:p:606-622

DOI: 10.1016/j.energy.2019.06.075

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().